The process performed by a typical face recognition system is shown in FIG. 1. The aim is to identify the person whose face is represented in the probe image 8 by comparing the probe image to a set of gallery images 14. Each image in the set of gallery images 14 includes the face of a person whose identity is known.
The size of the probe image 8 and gallery images 14 must be the same prior to feature extraction [3]. As such, the images are normally resized during pre-processing 10 to a common intermediate format (IF) size (e.g. small sized images are up-scaled to this IF size while large sized images are downscaled to this IF size).
The face matching method is previously tuned to work with that particular IF image size. Then this face matching method 12 is applied to each probe image 8 by comparison to each of the gallery images 14 to identify candidate matching images in the set of gallery images 14.
Face matching methods can be placed into two general families: holistic and local-feature based. In typical holistic methods, a single feature vector describes the entire face and the spatial relations between face characteristics (e.g. eyes) are rigidly kept. Examples of such systems include PCA and Fisherfaces [2]. In contrast, local-feature based methods describe each face as a set of feature vectors (with each vector describing a small part of the face), with relaxed constraints on the spatial relations between face parts [4]. Examples include systems based on elastic graph matching, hidden Markov models (HMMs) and Gaussian mixture models (GMMs) [4].
Local-feature based methods have the advantage of being considerably more robust against misalignment as well as variations in illumination and pose [4, 11]. As such, face recognition systems using local-feature based approaches are more suitable for dealing with faces obtained in surveillance contexts.
Post processing 16 is then performed on the results of the face matching method 12 such as referencing details of the people that were identified as candidate matches from an external database (not shown).
Finally, the likely identity information 18 of the candidate match(es) from the set of gallery images 14 are presented to the user.
The use of IF processing in typical face recognition systems leads to disadvantages in mismatched resolution comparisons which include:                (i) For images with low underlying resolution, upscaling does not introduce any new information, and can potentially introduce artifacts or noise. Also, upscaled images are blurry, which causes the extracted features to be very different than those obtained from the downscaled faces with high underlying resolution, resulting in a significant drop in recognition accuracy.        (ii) Downscaling reduces the amount of information available, thereby reducing the performance of the face matching method.        
Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.